PyLatheDB - A Library for Relational Keyword Search with Support to Schema References

Relational Keyword Search (R-KwS) systems enable naive/informal users to explore and retrieve information from relational databases without knowing schema details or query languages. These systems take the keywords from the input query, locate the elements of the target database that correspond to these keywords, and look for ways to "connect" these elements using the information on key/foreign key pairs. Although several such systems have been proposed, most of them only support queries whose keywords refer to the contents of the target database and only a few support queries in which keywords may also refer to elements of the database schema. We showcase PyLatheDB, a Python library for Relational Keyword Search with Support to Schema References. PyLatheDB is based on Lathe, an R-KwS framework that generalizes the well-known concepts of Query Matches (QMs) and Candidate Joining Networks (CJNs) to handle keywords referring to schema elements and introduces new algorithms to generate them. Lathe also introduced a novel approach to automatically select the CJNs that are more likely to represent the user intent when issuing a keyword query. This approach includes two major innovations: a ranking algorithm for selecting better QMs, yielding the generation of fewer but better CJNs, and an eager evaluation strategy for pruning useless CJNs. We demonstrate through a Jupyter ¹ notebook the functioning of PyLatheDB for two representative application scenarios, showing each step of the keyword query processing. The users can interact with the notebook by running keyword queries and experimenting with configuration parameters to see how they affect the results. The notebook, a video, and the code of PyLatheDB are available at https://github.com/pr3martins/PyLatheDB.